Determining when to perform error checking of a storage unit by training a machine learning module

ABSTRACT

Provided are a computer program product, system, and method for using a machine learning module to determine when to perform error checking of a storage unit. Input on attributes of at least one storage device comprising the storage unit are provided to a machine learning module to produce an output value. An error check frequency is determined from the output value. A determination is made as to whether the error check frequency indicates to perform an error checking operation with respect to the storage unit. The error checking operation is performed in response to determining that the error checking frequency indicates to perform the error checking operation.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a computer program product, system, andmethod for using a machine learning module to determine when to performerror checking of a storage unit.

2. Description of the Related Art

To maintain data integrity in a storage system, error checking and errorcorrection operations if errors are detected may be performed on thedata. For storage arrays, such as a Redundant Array of Independent Disks(RAID), parity data may be maintained that may be used to check whetherthere are data errors and to correct the data. In RAID arrays, a parityscrub operation may be periodically performed to verify integrity ofdata and parity blocks by reading all the blocks in a RAID stripe storedacross the storage devices in a RAID array, and comparing the read datato the parity data to determine if there are inconsistencies. Parityscrub operations are computationally expensive requiring systemcomputational resources and continual access to read data and paritydata from the storage array. As drive capacities have increased, theextent of the error checking operations for a parity scrub hasexperienced a corresponding increase, which may result in latency delaysfor other operations and applications seeking to access the data in theRAID array.

One technique for error checking is to periodically perform a parityscrub after a fixed number of write operations. For critical data,system administrators may set the fixed number low to ensure continualerror correction operations to avoid data errors propagating through thecritical data. However, these continual error checking operations maycause latency delays in accessing the critical data.

There is a need in the art for improved techniques for determining whento perform error correction checking and operations in a storage unit.

SUMMARY

A First Embodiment comprises a computer program product, system, andmethod for using a machine learning module to determine when to performerror checking of a storage unit. Input on attributes of at least onestorage device comprising the storage unit are provided to a machinelearning module to produce an output value. An error check frequency isdetermined from the output value. A determination is made as to whetherthe error check frequency indicates to perform an error checkingoperation with respect to the storage unit. The error checking operationis performed in response to determining that the error checkingfrequency indicates to perform the error checking operation.

The First Embodiment provides improvements to computer technology fordetermining when to perform error checking and handling by using amachine learning module and algorithm to dynamically determine thelikelihood of an error at a storage unit based on current operatingparameters recorded for a storage device. In this way, the describedembodiments optimize the allocation of computational and storageresources to error checking operations by dynamically determining afrequency at which to perform error checking operations that are basedon a prediction as to whether a data error is likely given currentoperating conditions at a storage device. This dynamic determinationbases a decision to perform error checking on a prediction as to whethera data error is likely given current operating conditions at a storagedevice.

In a second embodiment, the subject matter of the First and Thirdthrough Ninth Embodiments may optionally include that the storage unitcomprises one of an array of storage devices or one storage device.

The Second Embodiment extends the optimization technique to optimizeerror checking operations for storage arrays of storage devices, such asRAID arrays, and for single storage device storage units.

In a Third Embodiment, the subject matter of the First, Second andFourth through Ninth Embodiments may optionally include that theproviding the input on attributes of the storage unit comprisesperiodically providing the input to the machine learning module toperiodically produce the output value, wherein the operations ofdetermining the error check frequency and the determining whether theerror check frequency indicates to perform the error checking operation.

With the Third Embodiment, the determination of when to perform errorchecking using the machine learning algorithm to limit error checking tothose instances where there is a prediction of a likelihood of error isperiodically performed to continually determine whether error checkingis needed to optimize the error checking. This ensures that any periodsof increased error occurrences will be subject to error checking andhandling, and that periods with low error rates will not result in errorchecking because the checking will not be performed when the likelihoodof an error is low.

In a Fourth Embodiment, the subject matter of the First through Thirdand Fifth through Ninth Embodiments may optionally include that thedetermining the error check frequency from the output value comprisesdetermining a number of writes as a function of the output value. Thedetermining whether the error check frequency indicates to perform theerror checking operation comprises determining whether a write counterexceeds the error check frequency. The error checking operation isperformed in response to the write counter exceeding the error checkfrequency.

With the Fourth Embodiment, error checking operations are optimized bychecking for errors when the write counter exceeds the error checkingfrequency, which is adjusted based on a determination of a likelihood ofan error occurring with respect to the storage device. Thus, in thiscase error checking is again performed based on determined likelihood ofan error to ensure optimized allocation of computing resources betweenerror checking and other processes.

In a Fifth Embodiment, the subject matter of the First through Fourthand Sixth through Ninth Embodiments may optionally include resetting thewrite counter to zero in response to determining to perform the errorchecking operation.

With the Fifth Embodiment, the write counter is reset to allow a nextdetermination to perform error checking to occur at the error checkingfrequency rate, which is trained based on current operating conditions.

In a Sixth Embodiment, the subject matter of the First through Fifth andSeventh through Ninth Embodiments may optionally include that the outputvalue comprises a number from zero to 1 indicating a likelihood thatthere is an error in the storage unit. The determining whether the errorcheck frequency indicates to perform the error checking operationcomprises: not performing the error checking operation in response tothe output value being less than a lower bound; performing the errorchecking operation in response to the output value being greater than anupper bound; and adjusting the error check frequency based on the outputvalue in response to the output value being between the lower bound andthe upper bound.

With the Sixth Embodiment, the error checking operation is not performedto optimize resource allocation to other operations when the outputvalue indicates the likelihood of an error is below a low threshold andthe error checking operation is performed if there is a greaterlikelihood of an error, which ensures the use of resources areredirected to error checking during a time of likely increased errorrates. Further, the error checking frequency may be adjusted based onthe output value to further optimize resource allocation for futureerror checking resources.

In a Seventh Embodiment, the subject matter of the First through Sixth,Eighth, and Ninth embodiments may optionally include that the errorchecking operation checks a fixed number of last writes, and wherein theadjusting the error check frequency sets the error check frequency tothe fixed number of last writes divided by the output value.

With the Seventh Embodiment, only a limited number of writes are subjectto error checking to further limit the use of computational resources aspart of error checking.

In an Eighth Embodiment, the subject matter of the First through Seventhand Ninth Embodiments may optionally include that the attributes of theat least one storage used as the input to the machine learning moduleinclude at least one of: an error type if the error checking operationdetected an error during a last run of the error checking operation orindication of no error if the error checking operation did not detect anerror during the last run; a type of at least one storage devicecomprising the storage unit; an age of the at least one storage devicefrom first use; a firmware level of the at least one storage device; aread operations per second at the at least one storage device; and awrite operations per second at the at least one storage device.

With the Eighth Embodiment, attributes used to determine the likelihoodof an error include those attributes most likely to be predictive of anerror occurring to optimize the operation to determine when to performerror checking. For instance, the error type of a last error will havesignificantly predictive value if the error type is one that is likelyor not likely to reoccur. An usage level or age may be predictive, suchas older or more used storage devices may be more prone to errors. Thefirmware level may be predictive if a specific firmware level isassociated with greater error rates. Also, the number of read and writeoperations that are occurring may also be highly predictive, as errorsmay more frequently occur during high usage rates.

In a Ninth Embodiment, the subject matter of the First through EighthEmbodiments may optionally include incrementing a write counter inresponse to performing a write operation against the storage unit;determining whether the write counter satisfies a condition with respectto the error checking frequency; performing the error checking operationand resetting the write counter in response to determining that thewrite counter satisfies the condition with respect to the error checkingfrequency.

With the Ninth Embodiment, the error checking operation is scheduled tooccur when the number of writes indicated in the write counter satisfiesa condition of the error frequency, such as equaling the errorfrequency. At this time, an error check should be performed to determineif there are errors to correct.

A Tenth Embodiment comprises a computer program product, method, andsystem for error checking data in a storage unit. A determination ismade to train a machine learning module. In response to determining totrain the machine learning module, a determination is made of inputscomprising attributes of at least one storage device of the storageunit. The machine learning module is trained to produce a desired outputvalue indicating to perform an error checking operation of the storageunit from the determined inputs in response to detecting the error. Themachine learning module is executed to produce an output value used todetermine whether to perform an error checking operation with respect tothe storage unit.

The Tenth Embodiment improves computer technology for error checking byretraining a machine learning module to produce a desired output valuethat reflects the current likelihood of an error based on currentattributes of the storage device(s) to have the machine learning modulemore accurately predict a likelihood of an error at the storage unit,e.g., single storage device or array of storage devices. In this way,the Tenth Embodiment improves the accuracy in the machine learningmodule determining a likelihood of an error based on current operatingconditions and attributes.

In an Eleventh Embodiment, the subject matter of the Tenth and Twelfththrough Fifteenth Embodiments may optionally include detecting an errorwhile performing the error checking operation, wherein the determiningto train the machine learning module occurs in response to detecting theerror. The desired output value is set to an output value indicating toperform error checking to use to train the machine learning module inresponse to detecting the error.

With the Eleventh Embodiment, the machine learning module predictabilityis improved by retraining the module to determine a likelihood of anerror based on current operating conditions that are occurring when anerror was in fact detected. In this way, the machine learning module istrained during real time error detection to indicate an error to improvethe likelihood of predicting an error during actual error operatingconditions.

In a Twelfth Embodiment, the subject matter of the Tenth, Eleventh, andThirteenth through Fifteenth Embodiments may optionally includedetecting that an error has not been detected within a fixed number oferror checking operations. The determining to train the machine learningmodule occurs in response to detecting that the error has not beendetected within the fixed number of error checking operations. Thedesired output value is set to an output value indicating to not performerror checking to use to train the machine learning module in responseto detecting that an error has not been detected within the fixed numberof error checking operations.

With the Twelfth Embodiment, the machine learning module predictabilityis improved by retraining the module to determine a likelihood of noerror based on current operating conditions that are occurring whenthere has been no error detected during error checking. In this way, themachine learning module is trained during real time error detection toindicate a low likelihood of error to improve the likelihood ofpredicting an error during actual operating conditions when no error wasdetected.

In a Thirteenth Embodiment, the subject matter of the Tenth throughTwelfth and Fourteenth through Fifteenth Embodiments may optionally thatthe training the machine learning module comprises determining a marginof error of the output value of the machine learning module and thedesired output value. The margin of error and the inputs to trainweights and biases of nodes in the machine learning module to producethe desired output value.

In a Fourteenth Embodiment, the subject matter of the Tenth throughThirteenth and Fifteenth Embodiments may optionally include that themachine learning module produces output values from inputs from storagearrays of storage devices managed by storage controllers to provide thestorage controllers with output values based on the inputs from thestorage devices indicating whether the storage controllers shouldperform error checking operations with respect to the storage devices inthe storage arrays managed by the storage controllers.

With the Fourteenth Embodiment, network error prediction is improved byconsolidating the prediction with one machine learning module that maybe retrained based on errors occurring at all storage controllers. Thusretraining based on error detection at any storage controller in thenetwork will improve the predictability of determining the likelihood oferror when used for any storage controller in the network.

In a Fifteenth Embodiment, the subject matter of the Tenth throughFourteenth Embodiments may optionally include detecting a field errorindependent of the error checking operation, wherein the determining totrain the machine learning module occurs in response to detecting thefield error. The desired output value is set to an output valueindicating to perform error checking to use to train the machinelearning module in response to detecting the error.

With the Fifteenth Embodiment, the machine learning modulepredictability is improved by retraining the module to determine alikelihood of an error based on detecting a field error, such as adropped write, that was detected independently of the running of theerror checking. In this way, the machine learning module is trainedduring real time independent error detection to indicate an error toimprove the likelihood of predicting an error during actual erroroperating conditions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of a computing environment.

FIG. 2 illustrates an embodiment of storage device information.

FIG. 3 illustrates an embodiment of error checking information.

FIG. 4 illustrates an embodiment of operations to periodically run amachine learning module for error checking operations.

FIG. 5 illustrates an embodiment of operations to process a writerequest.

FIG. 6 illustrates an embodiment of operations to perform an errorchecking operation.

FIG. 7 illustrates an embodiment of operations to train the machinelearning module to determine a likelihood of an error in a storage unit.

FIG. 8 illustrates an embodiment of operations to perform field errorhandling.

FIG. 9 illustrates an additional embodiment of a computing environmentincluding a plurality of storage controllers and storage arrays.

FIG. 10 illustrates a computing environment in which the components ofFIGS. 1 and 8 may be implemented.

DETAILED DESCRIPTION

Prior art systems that periodically perform error checking operations ina storage array, such as a RAID array, direct resources and storageaccesses away from applications for the purpose of performing errorchecking and handling. This redirection of computational and storageresources may result in latency delays to applications trying to accessdata in the storage array. Prior art systems that periodically performerror checking operations at fixed intervals may perform error checkingmore frequently than needed if there are relatively few errors in thestorage array, thus needlessly causing latency delays. Further,periodically performing error checking operations at fixed intervals mayperform error checking less frequently than needed if there are agreater number of errors occurring in the storage array, which mayresult in data integrity errors in the system.

Described embodiments provide improved computer technology fordetermining when to perform error checking and handling operations thatuse a machine learning module and algorithm to dynamically determine thelikelihood of an error at a storage unit based on current operatingparameters recorded for a storage device. The machine learning modulemay continually be retrained to improve the predictive accuracy ofdetermining whether the storage device is experiencing errors using acurrent state of operational parameters and feedback on actualexperienced errors or lack of errors. In this way, the describedembodiments optimize the allocation of computational and storageresources to error checking operations by dynamically determining afrequency at which to perform error checking operations that are basedon a prediction as to whether a data error is likely given currentoperating conditions at a storage device.

FIG. 1 illustrates an embodiment of a computing environment. A computingsystem 100 accesses data in storage devices 102 in a storage array 104.The computing system 100 includes a processor 106 and a memory 108,including a cache 110 to cache data for the storage array 104. Theprocessor 106 may comprise one or more central processing units (CPUs)or a group of multiple cores on a single CPU. The cache 110 buffers datarequested by processes within the computing system. Alternatively, thecomputing system 100 may comprise a storage controller that processesInput/Output (I/O) access requests for tracks in the storage array 104from hosts 105 connecting to the computing system 100 (storagecontroller) over a network 107.

The memory 108 further includes a storage manager 112 to manage accessrequests from internal processes in the computing system 100 and/or fromhosts 105 for tracks in the storage array 104. A cache manager 114maintains accessed tracks in the cache 110 for future read access to thetracks to allow the accessed tracks to be returned from the fasteraccess cache 110 instead of having to retrieve from the storage array104. A track may comprise any unit of data configured in the storagearray 104, such as a track, Logical Block Address (LBA), etc., which ispart of a larger grouping of tracks, such as a volume, logical device,etc.

The memory 108 further includes an error checking module 116 to performerror checking operations, such as a parity check operation or parityscrub to determine data is valid according to parity or error checkingcodes (ECCs). If there are detected errors, the error checking module116 may perform error correction operations, such as correcting the datausing the parity information, correcting the parity information, fencingthe data, or providing notification to replace a storage device. Theerror checking module 116 maintains storage device information 200 thathas information on the storage devices 102 in the storage array 104 anderror checking information 300 used to determine whether to perform anerror checking operation and error handling. The error checking module116 may provide storage device information 200 as input 118 to a machinelearning module 120 to produce an output value 122 that indicates alikelihood that an error has occurred at the checked storage device 102requiring an error checking operation to be performed to determine ifthere is an error at the storage device 102, such as a parity error.

The machine learning module 120 may implement a machine learningtechnique such as decision tree learning, association rule learning,neural network, inductive programming logic, support vector machines,Bayesian models, etc., to receive as input 118 certain of the storagedevice information 200 and generate an output value 122 or confidencelevel, such as a value between 0 and 1, indicating a likelihood that thethere is an error at a storage unit, comprising one storage device or anarray 104 of storage devices 102.

In one embodiment, the machine learning module 120 may comprise anartificial neural network programs trained using back propagation toadjust weights and biases at nodes in a hidden layer of the firstartificial neural network program to produce an output value 122 basedon input 118 comprising storage device information 200. Back propagationmay comprise an algorithm for supervised learning of artificial neuralnetworks using gradient descent. Given an artificial neural network andan error function, the method may calculate the gradient of the errorfunction with respect to the neural network's weights and biases.

The storage manager 112, cache manager 114, the error checking module116, and the machine learning module 120 are shown in FIG. 1 as programcode loaded into the memory 108 and executed by the processor 106.Alternatively, some or all of the functions may be implemented inhardware devices in the system 100, such as in Application SpecificIntegrated Circuits (ASICs) or executed by separate dedicatedprocessors.

The storage array 104 may comprise one or more storage devices 102 knownin the art, such as a solid state storage device (SSD) comprised ofsolid state electronics, EEPROM (Electrically Erasable ProgrammableRead-Only Memory), flash memory, flash disk, Random Access Memory (RAM)drive, storage-class memory (SCM), Phase Change Memory (PCM), resistiverandom access memory (RRAM), spin transfer torque memory (STM-RAM),conductive bridging RAM (CBRAM), magnetic hard disk drive, optical disk,tape, etc. The storage devices 102 may further be configured into anarray of devices, such as Just a Bunch of Disks (JBOD), Direct AccessStorage Device (DASD), Redundant Array of Independent Disks (RAID)array, virtualization device, etc. Further, the storage devices maycomprise heterogeneous storage devices from different vendors or fromthe same vendor.

The memory 108 may comprise a suitable volatile or non-volatile memorydevices, including those described above.

The network 107 may comprise a Storage Area Network (SAN), a Local AreaNetwork (LAN), a Wide Area Network (WAN), the Internet, and Intranet,etc.

In RAID embodiments, the error checking operation may comprise a parityscrub, where the error checking module 116 reads blocks within a RAIDstripe of a RAID array configured within storage devices 102 andidentifies errors using the parity data in parity blocks, such asdropped writes, media errors, check sum errors, parity consistency, etc.

In FIG. 1, the machine learning module 120 is shown separate from theerror checking module 116. In further embodiments, some or allcomponents of the machine learning module 120 may be part of the errorchecking module 116.

FIG. 2 illustrates an embodiment of an instance of storage deviceinformation 200 _(i) for a storage device 102 _(i), and includes astorage device identifier (ID) 202 of a storage device 102 _(i); a lasterror check result 204, such as the result of the last time the errorchecking module 116 performed an error checking operation, which mayindicate no error, or a type of error, such as link error, hardwareerror, expander errors showing dropped links, dropped write, powererror, etc.; a manufacturer 206 of the storage device 202; an age 208,which may be measured as a time since first used or measured by a numberof writes that have occurred, e.g., wear; a firmware level 210 of thestorage device 202; read operations (“OPS”) per second 212 rate measuredfor a last number of writes (N) tracked at the storage device 202; andwrite operations per second 214 rate measured for the last number ofwrites (N) tracked at the storage device 202. The storage deviceinformation 200 _(i) includes static information, such as 202, 206, 210,and dynamic information that may be continually updated, such as inblocks 204, 208, 212, and 214. For instance the last error check result204 may be updated each time an error checking operation is performed atthe storage device 102 _(i) and the read 212 and write 214 operationsper second and age 208 may be updated after one or more read/writeoperations.

FIG. 3 illustrates an instance of error checking information 300 _(i)maintained for a storage unit, such as a RAID rank of storage devices102 or one storage device 102 _(i), that includes: a storage unit ID302, e.g., RAID rank ID or a storage device ID 102 _(i); a write counter304 indicating a number of writes that have occurred since the lasterror checking operation with respect to the storage unit 302; an errorcheck frequency 306 indicating a number of writes that must occur beforean error checking operation at the storage unit 302 is to be performed;a last output value 308 calculated by the machine learning module 120for the storage unit 302; and an error checking counter 310 indicating anumber of error checking operations that have occurred at the storageunit 302 without detecting an error.

With the embodiment of FIG. 3, an instance of the error checkinginformation 300 _(i) may be maintained for each of the storage devices102 _(i), i.e., storage device specific error checking information, ormaintained for all the storage devices in a storage unit, such as a RAIDrank, where a write stripes data across multiple of the storage devices.

FIG. 4 illustrates an embodiment of operations performed by the errorchecking module 116 and/or machine learning module 120 to periodicallyrun the machine learning module 120 to determine whether to perform anerror checking operation or adjust the error checking frequency 306. Themachine learning module 120 may be periodically run at time intervals orin response to events. The machine learning module 120 may be invokedseparately for each of storage devices 102 or for a RAID rank to trainfor all the storage devices 102 in a RAID rank (or other type of storagearray). Upon invoking (at block 400) the machine learning module 120 fora storage unit, which may comprise one storage device 102 _(i) or a RAIDrank or storage array of a plurality of storage devices 102, inputs 118are determined (at block 402) from the storage device information 200_(i) for one or more storage devices 102 _(i) comprising the storageunit. In an embodiment where the error checking information 300 _(i) isprovided for an individual storage device 102 _(i), then the machinelearning module 120 may receive as input 118 the storage deviceinformation 200 _(i) for just one storage device 102 _(i). In anembodiment where the error checking information 300 _(i) is provided fora RAID rank of storage devices, then the machine learning module 120 mayreceive as input 118 the storage device information 200 _(i) for all ofthe storage devices 102 in the RAID rank.

The machine learning module 120 is called (at block 404) with thedetermined inputs 118 to produce an output value 122 indicating alikelihood of an error occurring at the storage unit (i.e., storagedevice or storage/RAID array). In one embodiment, the output value 122may be between 0 and 1, with a value closer to 0 indicating a lowerlikelihood of an error occurring at the storage unit and a value closerto 1 indicating a higher likelihood of an error occurring at the storageunit.

If (at block 406) the output value is less than a lower bound, which mayindicate a low likelihood of a data error, then control ends. If (fromthe no branch of block 406) the output value is higher than the lowerbound but less (from the no branch of block 408) than an upper bound,then the error checking frequency 306 for the storage unit may beadjusted (at block 410) based on the output value. In one embodiment,the error checking frequency 306 may be adjusted by setting the errorchecking frequency 306 to a fixed number (N) of last writes to errorcheck divided by the output value. In alternative embodiments, othercalculations and variables may be considered with the output value toadjust the error checking frequency 306. If (at block 408) the outputvalue is greater than an upper bound, indicating a greater likelihood ofan error at the storage unit, then control proceeds (at block 412) toFIG. 6 to perform an error checking operation, e.g., parity check, withrespect to the last N writes at the storage unit, e.g., one storagedevice or a rank of storage devices 102.

With the embodiment of FIG. 4, the machine learning module 120 is run todetermine a likelihood that there is an error in a storage unit based onactual operating conditions of the one or more storage devices in thestorage unit. This likelihood is based on a trained machine learningalgorithm that bases the determination of the likelihood of an error oncurrent operating conditions at the storage devices 102 and statisticaland probabilistic models that relate such operating conditions of thestorage devices 102 to a likelihood of an error. In this way, adetermination of an error checking frequency is adjusted based on thelikelihood of an error so that the error checking frequency 306 isincreased, i.e., the frequency of number of writes is decreased, ifthere is a greater likelihood of an error and the error checkingfrequency 306 is reduced, i.e., the frequency of the number of writes isincreased, if there is less of a likelihood of an error at the storageunit. Because the error checking operations, such as a parity scrub forRAID array storage units, consume substantial processing resources,which may cause latency in other operations, adjusting the errorchecking frequency based on actual error conditions error improves theallocation of system resources. If there is a low likelihood of anerror, then the error checking frequency may be reduced to reduce thenumber of error checking operations, thus freeing resources to reducelatency for other operations. However, if there is a higher likelihoodof an error, then the error checking frequency may be increased becausethe importance of the benefits of correcting data errors offsets thenegative impact of increased latency for other operations. In this way,the use of the machine learning module 120 to adjust the error checkingfrequency optimizes the allocation of resources between error checkingand other operations.

FIG. 5 illustrates an embodiment of operation performed by the storagemanager 112 to process a write request to a storage unit (storage deviceor RAID rank of storage devices). Upon receiving (at block 502) a writerequest, the write request is processed (at block 504) such as writtento a storage device 102 _(i) or striped across a rank of storage devices102. The write counter 304 for the storage unit is incremented (at block506) and the storage device information 200 _(i) for the one or morestorage devices in the storage unit is updated (at block 508), such asin fields 208, 212, 214. If (at block 510) the write counter 304 isgreater than or equal to the error checking frequency 306, then thewrite counter 304 is reset (at block 512) and control proceeds (at block514) to FIG. 6 to perform an error checking operation, e.g., paritycheck, with respect to the last N writes at the storage unit, e.g., onestorage device or a rank of storage devices 102. If (at block 510) thewrite counter is less than the error checking frequency 306, thencontrol ends.

In FIG. 5, the determination of whether to error check is based onwhether the write counter 304 is greater than or less than the errorchecking frequency 306. In alternative embodiments, other conditions orrelationships between the write counter 304 and error checking frequency306 may be used to determine whether to error check or not.

With the embodiment of FIG. 5, error checking operations are optimizedby checking for errors when the write counter exceeds the error checkingfrequency 306, which is adjusted based on a determination of alikelihood of an error occurring with respect to the storage device.

FIG. 6 illustrates an embodiment of error checking operations performedby the error checking module 116 that may be invoked at block 412 inFIG. 4 when the machine learning module 120 produces a high output value122 and at block 514 in FIG. 5 if the write counter 304 exceeds theerror checking frequency 306. Upon initiating (at block 600) errorchecking operations, the error checking counter 310 is incremented (atblock 602) and an error checking operations is performed (at block 604)on the fixed number of last writes (N) on the storage unit (one or morestorage devices 102). The error checking operation may comprise a paritychecking or error correction code checking operation. If (at block 606)an error is detected, then the error checking counter 310, indicating anumber of consecutive error checking operations with no error, is reset(at block 608). The error checking module 116 performs (at block 610) anerror handling operation to correct error in data and/or the parityinformation for the last N writes checked. The error handling maycorrect data in the last N writes from the parity information or correctthe parity information from the data. After performing error handling,control proceeds (at block 612) to FIG. 7 to train the machine learningmodule 120 for the storage unit (device or rank of storage deviceshaving error) with a desired output value comprising a highest outputvalue indicating to perform the error checking operation, e.g., adesired output value of one.

If (at block 606) an error is not detected, then the error checkingcounter 310, indicating a number of consecutive error checkingoperations with no error, is incremented (at block 614). A determinationis made (at block 616) whether the error checking counter 310 is greaterthan or equal to an error free threshold number of error checkingoperations. If (at block 616) the error checking counter exceeds theerror free threshold, i.e. there has been a threshold number of errorchecking operations with no errors, then the error checking counter 318is reset (at block 618) and control proceeds (at block 620) to FIG. 7 totrain the machine learning module 120 for storage unit (device or rankof storage devices having error) with the desired output valuecomprising a low threshold value, indicating to not perform the errorchecking operation, such as 0.001. If (at block 616) the error checkingcounter 310 does not exceed the error free threshold, then control endswithout retraining the machine learning module 120 to decrease theoutput value, because there have not been a sufficient number of errorfree error checking operations to warrant adjusting the output valuedownward.

With the embodiment of FIG. 6, after performing error checking, themachine learning module 120 is trained to produce an output valueindicating a high likelihood of error from the current operatingconditions at the storage unit that resulted in the error to increasethe likelihood that the machine learning module 120 can accuratelypredict an error when similar operating conditions arise in the future.Likewise, if an error has not occurred after a predefined number oferror checking operations, then the machine learning module 120 istrained to produce an output value indicating a low likelihood of errorfrom the current operating conditions at the storage unit that resultedin no error occurring for several error checking operations to increasethe likelihood that the machine learning module 120 can accuratelypredict no error when similar operating conditions occur in the future.

FIG. 7 illustrates an embodiment of operations performed by the errorchecking module 116 and/or machine learning module 120 to retrain themachine learning module 120 to produce a desired output value, such as ahigher or lower output value depending on whether an error has beendetected (block 610 in FIG. 6) or not detected a threshold number oftimes (from block 620 in FIG. 6). Upon initiating (at block 700) anoperation to train the machine learning module 120 to produce a desiredoutput value for storage unit (storage device or rank of storagedevices), a determination is made (at block 702) of the inputs from thecurrent storage device information 200 _(i) for the one or more storagedevices in the storage unit for which the machine learning module 120 isbeing trained. The machine learning module 120 is run (at block 704)with the determined inputs to produce a current output value 122. Adetermination is made (at block 706) of a margin of error of the desiredoutput value and the current output value 122. The machine learningmodule 120 is trained (at block 708) using the determined inputs 118 andthe margin of error, and other information, to produce the desiredoutput value. The machine learning module 120 may be trained using backpropagation to reduce the margin of error, to produce the desired outputvalue. In embodiments where the machine learning module 120 algorithmcomprises an artificial neural network, a backward propagation routinemay be used to retrain the machine learning module 120 to produce thedesired output value 122. For other types of machine learningalgorithms, such as Bayesian models, other techniques may be used toretrain the machine learning module 120 to produce the desired outputvalue. The settings, e.g., adjusted weights and biases of the hiddenlayer of the machine learning module 120, are then saved (at block 710)for later us.

With the embodiment of FIG. 7, the machine learning module 120 isretrained to produce a desired output value that reflects the currentlikelihood of an error based on current attributes of the storagedevice(s) to have the machine learning module 120 more accuratelypredict a likelihood of an error at the storage unit, e.g., singlestorage device or array of storage devices.

In the embodiments of FIGS. 1-7, the machine learning module 120determines a likelihood of error at one or more storage devices for asingle computer system and storage array 104 having one or more RAIDsranks.\

FIG. 8 illustrates an embodiment of operations performed by the errorchecking module 116 and/or machine learning module 120 to handledetected field error, such as a dropped error or other errorindependently encountered. Upon initiating (at block 802) field errorhandling a field error, such as a dropped write, is detected (at block806) the error checking counter 310 is reset (at block 806). Errorhandling operations are performed (at block 808) to correct an error indata and/or parity for the last N writes, or fence the storage device102 having the error and rebuild data no a new storage device. Controlthen proceeds (at block 810) to FIG. 7 to train the machine learningmodule 120 for the storage unit (device or rank of storage deviceshaving the error) with the desired output value comprising a highestoutput value indicating to perform the error checking operation, due tothe detection of the field error.

With the embodiment of FIG. 8, the machine learning module is trainedupon detecting a field error outside of the error checking operation ofFIG. 6 so as to retrain the machine learning module 120 to output avalue indicating to perform error checking if the inputs 118 at the timeof the detected field error occur.

FIG. 9 illustrates an additional embodiment where the computing system100 described with respect to FIGS. 1-8 is in communication with aplurality of storage controllers 900 ₁, 900 ₂ . . . 900 _(m) eachmanaging access to a storage array 902 ₁, 902 ₂ . . . 902 _(m) over anetwork 904. The machine learning module 120 receives inputs from one ormore storage devices in a storage array 902 ₁, 902 ₂ . . . 902 _(m) tocalculate an output value for the storage controller 900 ₁, 900 ₂ . . .900 _(m) managing the storage array 902 ₁, 902 ₂ . . . 902 _(m) to useto determine whether to initiate an error checking and correctionoperation. In this way, the machine learning module 120 may be morefrequently and hence more accurate because it is trained from storagedevices in multiple storage arrays 902 ₁, 902 ₂ . . . 902 _(m). Althoughthe machine learning module 120, storage device information 200 anderror checking information 300 may be maintained in the computing system100, the error checking module 116 may be maintained locally in each ofthe storage controllers 900 ₁, 900 ₂ . . . 900 _(m).

In the described embodiment, variables “i, m, n”, etc., when used withdifferent elements may denote a same or different instance of thatelement.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computational components of FIGS. 1 and 8, including the hosts 105,computing system 100, and storage controllers 900 ₁, 900 ₂ . . . 900_(m) may be implemented in one or more computer systems, such as thecomputer system 1002 shown in FIG. 10. Computer system/server 1002 maybe described in the general context of computer system executableinstructions, such as program modules, being executed by a computersystem. Generally, program modules may include routines, programs,objects, components, logic, data structures, and so on that performparticular tasks or implement particular abstract data types. Computersystem/server 1002 may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

As shown in FIG. 10, the computer system/server 1002 is shown in theform of a general-purpose computing device. The components of computersystem/server 1002 may include, but are not limited to, one or moreprocessors or processing units 1004, a system memory 1006, and a bus1008 that couples various system components including system memory 1006to processor 1004. Bus 1008 represents one or more of any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limitation, such architectures include Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 1002 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 1002, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 1006 can include computer system readable media in theform of volatile memory, such as random access memory (RAM) 1010 and/orcache memory 1012. Computer system/server 1002 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 1013 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 1008 by one or more datamedia interfaces. As will be further depicted and described below,memory 1006 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 1014, having a set (at least one) of program modules1016, may be stored in memory 1006 by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystem, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. The components of the computer 1002 may beimplemented as program modules 1016 which generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein. The systems of FIG. 1 may be implemented in one ormore computer systems 1002, where if they are implemented in multiplecomputer systems 1002, then the computer systems may communicate over anetwork.

Computer system/server 1002 may also communicate with one or moreexternal devices 1018 such as a keyboard, a pointing device, a display1020, etc.; one or more devices that enable a user to interact withcomputer system/server 1002; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 1002 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 1022. Still yet, computer system/server1002 can communicate with one or more networks such as a local areanetwork (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter 1024. As depicted,network adapter 1024 communicates with the other components of computersystem/server 1002 via bus 1008. It should be understood that althoughnot shown, other hardware and/or software components could be used inconjunction with computer system/server 1002. Examples, include, but arenot limited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the present inventionneed not include the device itself.

The foregoing description of various embodiments of the invention hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe invention. Since many embodiments of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims herein after appended.

What is claimed is:
 1. A computer program product for error checkingdata in a storage unit, the computer program product comprising acomputer readable storage medium storing computer readable program codethat when executed performs operations, the operations comprising: inresponse to detecting an error at the storage unit, performing:determining inputs comprising attributes of at least one storage deviceof the storage unit; and training a machine learning module, by trainingweights and biases of nodes in the machine learning module, to processthe determined inputs to produce an output value comprising a numericalvalue indicating a likelihood that there is an error in the storageunit, wherein an error checking operation of the storage unit isperformed when the numerical value indicates the likelihood that thereis the error; and executing the machine learning module to produce afurther output value comprising a numerical value indicating alikelihood that there is an error in the storage unit and used todetermine whether to perform an error checking operation with respect tothe storage unit.
 2. The computer program product of claim 1, whereinthe operations further comprise: detecting that an error has not beendetected within a fixed number of error checking operations, wherein thedetermining inputs and training the machine learning module areperformed in response to detecting that the error has not been detectedwithin the fixed number of error checking operations.
 3. The computerprogram product of claim 1, wherein the training the machine learningmodule comprises: executing the machine learning module to produce acurrent output value; determining a margin of error of the currentoutput value and the numerical value indicating the likelihood thatthere is the error in the storage unit; and using the margin of errorand the inputs to train the weights and the biases of the nodes in themachine learning module to produce the numerical value.
 4. The computerprogram product of claim 1, wherein the machine learning module producesoutput values from inputs of storage arrays of storage devices managedby storage controllers, wherein the output values from the machinelearning module are provided to the storage controllers, wherein thestorage controllers perform error checking operations with respect tothe storage devices in the storage arrays managed by the storagecontrollers when the output values indicate the likelihood that there isthe error.
 5. The computer program product of claim 1, wherein theoperations further comprise: detecting a field error independent of theerror checking operation, wherein the determining inputs and trainingthe machine learning module are performed in response to detecting thefield error.
 6. The computer program product of claim 1, wherein thenumerical value is from zero to 1 indicating a likelihood that there isan error in the storage unit, wherein the output value indicates to: notperform the error checking operation in response to the output valuebeing less than a lower bound; perform the error checking operation inresponse to the output value being greater than an upper bound; andadjust an error check frequency based on the output value in response tothe output value being between the lower bound and the upper bound. 7.The computer program product of claim 1, wherein the attributes of theat least one storage device used as input to the machine learning moduleinclude at least one of: an error type if the error checking operationdetected an error during a last run of the error checking operation orindication of no error if the error checking operation did not detect anerror during the last run; a type of at least one storage devicecomprising the storage unit; an age of the at least one storage devicefrom first use; a firmware level of the at least one storage device; aread operations per second at the at least one storage device; and awrite operations per second at the at least one storage device.
 8. Asystem for error checking data in a storage unit, comprising: aprocessor; and a computer readable storage medium storing computerreadable program code that when executed performs operations, theoperations comprising: in response to detecting an error at the storageunit, performing: determining inputs comprising attributes of at leastone storage device of the storage unit; and training a machine learningmodule, by training weights and biases of nodes in the machine learningmodule, to process the determined inputs to produce an output valuecomprising a numerical value indicating a likelihood that there is anerror in the storage unit, wherein an error checking operation of thestorage unit is performed when the numerical value indicates thelikelihood that there is the error; and executing the machine learningmodule to produce a further output value comprising a numerical valueindicating a likelihood that there is an error in the storage unit andused to determine whether to perform an error checking operation withrespect to the storage unit.
 9. The system of claim 8, wherein theoperations further comprise: detecting that an error has not beendetected within a fixed number of error checking operations, wherein thedetermining inputs and training the machine learning module areperformed in response to detecting that the error has not been detectedwithin the fixed number of error checking operations.
 10. The system ofclaim 8, wherein the training the machine learning module comprises:executing the machine learning module to produce a current output value;determining a margin of error of the current output value and thenumerical value indicating the likelihood that there is the error in thestorage unit; and using the margin of error and the inputs to train theweights and the biases of the nodes in the machine learning module toproduce the numerical value.
 11. The system of claim 8, wherein themachine learning module produces output values from inputs of storagearrays of storage devices managed by storage controllers, wherein theoutput values from the machine learning module are provided to thestorage controllers, wherein the storage controllers perform errorchecking operations with respect to the storage devices in the storagearrays managed by the storage controllers when the output valuesindicate the likelihood that there is the error.
 12. The system of claim8, wherein the operations further comprise: detecting a field errorindependent of the error checking operation, wherein the determininginputs and training the machine learning module are performed inresponse to detecting the field error.
 13. The system of claim 8,wherein the numerical value is from zero to 1 indicating a likelihoodthat there is an error in the storage unit, wherein the output valueindicates to: not perform the error checking operation in response tothe output value being less than a lower bound; perform the errorchecking operation in response to the output value being greater than anupper bound; and adjust an error check frequency based on the outputvalue in response to the output value being between the lower bound andthe upper bound.
 14. The system of claim 8, wherein the attributes ofthe at least one storage device used as input to the machine learningmodule include at least one of: an error type if the error checkingoperation detected an error during a last run of the error checkingoperation or indication of no error if the error checking operation didnot detect an error during the last run; a type of at least one storagedevice comprising the storage unit; an age of the at least one storagedevice from first use; a firmware level of the at least one storagedevice; a read operations per second at the at least one storage device;and a write operations per second at the at least one storage device.15. A method for error checking data in a storage unit, comprising: inresponse to detecting an error at the storage unit, performing:determining inputs comprising attributes of at least one storage deviceof the storage unit; and training a machine learning module, by trainingweights and biases of nodes in the machine learning module, to processthe determined inputs to produce an output value comprising a numericalvalue indicating a likelihood that there is an error in the storageunit, wherein an error checking operation of the storage unit isperformed when the numerical value indicates the likelihood that thereis the error; and executing the machine learning module to produce afurther output value comprising a numerical value indicating alikelihood that there is an error in the storage unit and used todetermine whether to perform an error checking operation with respect tothe storage unit.
 16. The method of claim 15, further comprising:detecting that an error has not been detected within a fixed number oferror checking operations, wherein the determining inputs and trainingthe machine learning module are performed in response to detecting thatthe error has not been detected within the fixed number of errorchecking operations.
 17. The method of claim 15, wherein the trainingthe machine learning module comprises: executing the machine learningmodule to produce a current output value; determining a margin of errorof the current output value and the numerical value indicating thelikelihood that there is the error in the storage unit; and using themargin of error and the inputs to train the weights and the biases ofthe nodes in the machine learning module to produce the numerical value.18. The method of claim 15, wherein the machine learning module producesoutput values from inputs of storage arrays of storage devices managedby storage controllers, wherein the output values from the machinelearning module are provided to the storage controllers, wherein thestorage controllers perform error checking operations with respect tothe storage devices in the storage arrays managed by the storagecontrollers when the output values indicate the likelihood that there isthe error.
 19. The method of claim 15, further comprising: detecting afield error independent of the error checking operation, wherein thedetermining inputs and training the machine learning module areperformed in response to detecting the field error.
 20. The method ofclaim 15, wherein the numerical value is from zero to 1 indicating alikelihood that there is an error in the storage unit, wherein theoutput value indicates to: not perform the error checking operation inresponse to the output value being less than a lower bound; perform theerror checking operation in response to the output value being greaterthan an upper bound; and adjust an error check frequency based on theoutput value in response to the output value being between the lowerbound and the upper bound.
 21. The method of claim 15, wherein theattributes of the at least one storage device used as input to themachine learning module include at least one of: an error type if theerror checking operation detected an error during a last run of theerror checking operation or indication of no error if the error checkingoperation did not detect an error during the last run; a type of atleast one storage device comprising the storage unit; an age of the atleast one storage device from first use; a firmware level of the atleast one storage device; a read operations per second at the at leastone storage device; and a write operations per second at the at leastone storage device.